Maintaining storage of encoded data slices

ABSTRACT

A method for execution by a computing device of a storage network includes determining an encoded data slice reduction scheme for a set of encoded data slices stored in a set of storage units of the storage network, where a data segment of data is encoded into the set of encoded data slices in accordance with encoding parameters, and where the encoding parameters include a pillar width number and a decode threshold number. The method further includes maintaining storage of the set of encoded data slices in accordance with the encoded data slice reduction scheme, where the maintaining storage includes keeping, until a deletion time for the set of encoded data slices, a number of encoded data slices of the set of encoded data slices equal to or greater than the decode threshold number and less than the pillar width number.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent application claims priority pursuant to35 U.S.C. § 120 as a continuation of U.S. Utility application Ser. No.16/392,126 entitled “DATA ACCESS OPTIMIZATION PROTOCOL IN A DISPERSEDSTORAGE NETWORK,” filed Apr. 23, 2019, issuing as U.S. Pat. No.10,802,915 on 10/13/2020, which is a continuation-in-part of U.S.Utility application Ser. No. 15/843,637, entitled “DATA ACCESSOPTIMIZATION PROTOCOL IN A DISPERSED STORAGE NETWORK,” filed Dec. 15,2017, issued as U.S. Pat. No. 10,289,342 on May, 14, 2019, which is acontinuation-in-part of U.S. Utility application Ser. No. 15/671,746,entitled “STORING AND RETRIEVING DATA USING PROXIES,” filed Aug. 8,2017, issued as U.S. Pat. No. 10,740,180 on 08/11/2020, which is acontinuation-in-part of U.S. Utility application Ser. No. 14/955,200,entitled “STORING DATA USING A DUAL PATH STORAGE APPROACH,” filed Dec.1, 2015, issued as U.S. Pat. No. 9,740,547 on Aug. 22, 2017, whichclaims priority pursuant to 35 U.S.C. § 119(e) to U.S. ProvisionalApplication No. 62/109,700, entitled “REDUNDANTLY STORING DATA IN ADISPERSED STORAGE NETWORK,” filed Jan. 30, 2015, all of which are herebyincorporated herein by reference in their entirety and made part of thepresent U.S. Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is an example of time-based storage of a set of encoded dataslices of a dispersed storage network (DSN) in accordance with thepresent invention;

FIG. 10 is a schematic block diagram of an example of time-based storageof encoded data slices in accordance with the present invention; and

FIG. 11 is a logic diagram of an example of a method of time-basedstorage of encoded data slices in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is an example of time-based storage of a set of encoded dataslices of a dispersed storage network (DSN). When a data object isdispersed storage error encoded into a plurality of sets of encoded dataslices and stored in the DSN, certain time parameters may be setregarding the data. For example, storage of the data object may only benecessarily for a period of time. In that case, a deletion time for thedata object is set such that all encoded data slices of the data objectare deleted from the DSN at the deletion time in order to free upstorage space. At a certain point prior to the deletion time, a lowerlevel of storage reliability may be acceptable to further free upstorage space prior to the deletion time.

FIG. 9 shows a graph of time versus reliability of storage. Upon storageof a set of encoded data slices (EDSs) (e.g., of the plurality of setsof EDSs of a data object) in one or more sets of storage units of theDSN, the reliability of storage is high. For example, a pillar widthnumber of encoded data slices of the set of encoded data slices areinitially stored in the one or more sets of storage units of the DSN.The pillar width is the total number of encoded data slices in the setof encoded data slices. At the time of storage, a deletion time 84 forthe set of encoded data slices is set. Setting the deletion time 84 isbased on one or more of a type of data of the set of encoded data slices(e.g., the type of data is meant for short term storage), a userassociated with the set of encoded data slices, storage capacity of theone or more sets of storage units, an instruction (e.g., from a user,system administrator, etc.), and a predetermination.

Also, at the time of storage, an EDS reduction time 82 is set at a timeprior to the deletion time 84. The EDS reduction time 82 is based on oneor more of the deletion time 84, a type of data of the set of encodeddata slices, a user associated with the set of encoded data slices,storage capacity of the one or more sets of storage units, aninstruction, and a predetermination. Upon expiration of the EDSreduction time 82, a computing device of the DSN (e.g., computing device12 or 16, not shown), implements an EDS reduction scheme 86. The EDSreduction scheme 86 includes one or more of an explicit deletion ofencoded data slices and a reduced rebuild operation.

The explicit deletion of encoded data slices includes deleting encodeddata slices over a period of time up until the deletion time 84 suchthat a remaining number of encoded data slices of the set of encodeddata slices is equal to or exceeds a decode threshold (DT) number and isless than a pillar width (PW) number. The decode threshold number is anumber of encoded data slices required to reconstruct a data segment ofa data object. As time gets closer to deletion time 84, it may beacceptable to store only a decode threshold number of encoded dataslices or less in the one or more sets of storage units.

Implementing the reduced rebuild operation includes determining areduced rebuild number of EDSs such that the reduced rebuild numberexceeds the decode threshold (DT) number and is less than pillar width(PW) number. When less than the reduced rebuild number of slices isremaining, the reduced rebuild operation is triggered such that one ormore encoded data slices of the set of encoded data slices are rebuilt.Prior to the expiration of the EDS reduction time, a full rebuildoperation may be implemented. For example, if less than a pillar widthnumber of encoded data slices are remaining, a full rebuild operation istriggered such that one or more encoded data slices of the set ofencoded data slices are rebuilt.

FIG. 10 is a schematic block diagram of an example of time-based storageof encoded data slices that includes a set of storage units #1-#7 36storing a set of encoded data slices (EDS 1_1, EDS 2_1, EDS 3_1, EDS4_1, EDS 5_1, EDS 6_1, and EDS 7_1) upon initial storage at a time T0.In this example, the pillar width (PW) is 7, and the decode threshold(DT) is 3.

As discussed with reference to FIG. 9, upon initial storage, a deletiontime and an EDS reduction time are set based on various factors such asstorage capacity and the type of data. The EDS reduction time occurs ata time prior to the deletion time. Upon expiration of the EDS reductiontime, an EDS reduction scheme is implemented. In this example, the EDSreduction scheme includes two explicit deletions at times T1 and T2 anda reduced rebuild operation. In this example, the reduced rebuild numberis 4.

At time T0, the set of storage units #1-#7 are storing a full pillarwidth number of encoded data slices for maximum storage reliability.Upon expiration of the EDS reduction time at time T1, the EDS reductionscheme indicates an explicit deletion of EDS 2_1 from SU #2. At anothertime T2, the EDS reduction scheme indicates an explicit deletion of EDS3_1 from SU #3 and EDS 4_1 from SU #4. At a time T3, a storage erroroccurs in SU #5 and EDS 5_1 is lost. Because the remaining number ofslices is less that the reduced rebuild number (4), and the EDSreduction scheme indicates a reduced rebuild, EDS 5_1 is rebuilt using adecode number of slices from the other storage units. At T5, thedeletion time is reached, and the set of encoded data slices are deletedfrom the set of storage units.

As an alternative example, the reduced rebuild operation may only betriggered at a certain time interval between the EDS reduction time andthe deletion time. For example, if the storage error in SU #5 occurredat a time close to the deletion (e.g., T3 is relatively close to T5),the EDS reduction scheme may indicate that a rebuild is not required inthat situation because the encoded data slices are close to deletion.

FIG. 11 is a logic diagram of an example of a method of time-basedstorage of encoded data slices. The method begins at step 88 where uponstorage of a set of encoded data slices (EDSs) in one or more sets ofstorage units of a dispersed storage network (DSN), a computing deviceof the DSN (e.g., computing device 12 or 16) sets a deletion time forthe set of encoded data slices. For example, storage of a data objectmay only be necessarily for a period of time. In that case, a deletiontime for the data object is set such that, at the deletion time, allencoded data slices of the data object are deleted from the DSN in orderto free up storage space. At a certain point prior to the deletion time,a lower level of storage reliability may be acceptable to further freeup storage space prior to the deletion time.

Setting the deletion time is based on one or more of a type of data ofthe set of encoded data slices (e.g., the type of data is meant forshort term storage), a user associated with the set of encoded dataslices, storage capacity of the one or more sets of storage units, aninstruction (e.g., from a user, system administrator, etc.), and apredetermination.

The method continues with step 90 where the computing device sets anencoded data slice reduction time for the set of encoded data slices.The encoded data slice reduction time is set at a time prior to thedeletion time. Setting the encoded data slice reduction time is based onone or more of the deletion time, a type of data of the set of encodeddata slices, a user associated with the set of encoded data slices,storage capacity of the one or more sets of storage units, aninstruction, and a predetermination.

The method continues with step 92 where upon expiration of the encodeddata slice reduction time, the computing device implements an encodeddata slice reduction scheme. The encoded data slice reduction schemeincludes one or more of an explicit deletion of encoded data slices ofthe set of encoded data slices and a reduced rebuild operation.

The explicit deletion of encoded data slices includes deleting encodeddata slices over a period of time up until the deletion time such that aremaining number of encoded data slices of the set of encoded dataslices is equal to or exceeds a decode threshold number and is less thana pillar width number.

Implementing the reduced rebuild operation includes determining areduced rebuild number of encoded data slices such that the reducedrebuild number exceeds the decode threshold number and is less thanpillar width number. When less than the reduced rebuild number of slicesis remaining, the reduced rebuild operation is triggered such that oneor more encoded data slices of the set of encoded data slices arerebuilt. Prior to the expiration of the encoded data slice reductiontime, a full rebuild operation may be implemented. For example, if lessthan a pillar width number of encoded data slices are remaining, a fullrebuild operation is triggered such that one or more encoded data slicesof the set of encoded data slices are rebuilt.

When the deletion time is reached, the set of encoded data slices aredeleted from the one or more sets of storage units of the DSN.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, text, graphics, audio, etc. any of which may generally bereferred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. For some industries, anindustry-accepted tolerance is less than one percent and, for otherindustries, the industry-accepted tolerance is 10 percent or more. Otherexamples of industry-accepted tolerance range from less than one percentto fifty percent. Industry-accepted tolerances correspond to, but arenot limited to, component values, integrated circuit process variations,temperature variations, rise and fall times, thermal noise, dimensions,signaling errors, dropped packets, temperatures, pressures, materialcompositions, and/or performance metrics. Within an industry, tolerancevariances of accepted tolerances may be more or less than a percentagelevel (e.g., dimension tolerance of less than +/−1%). Some relativitybetween items may range from a difference of less than a percentagelevel to a few percent. Other relativity between items may range from adifference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operablycoupled to”, “coupled to”, and/or “coupling” includes direct couplingbetween items and/or indirect coupling between items via an interveningitem (e.g., an item includes, but is not limited to, a component, anelement, a circuit, and/or a module) where, for an example of indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operableto”, “coupled to”, or “operably coupled to” indicates that an itemincludes one or more of power connections, input(s), output(s), etc., toperform, when activated, one or more its corresponding functions and mayfurther include inferred coupling to one or more other items. As maystill further be used herein, the term “associated with”, includesdirect and/or indirect coupling of separate items and/or one item beingembedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing circuitry”, and/or “processing unit”may be a single processing device or a plurality of processing devices.Such a processing device may be a microprocessor, micro-controller,digital signal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, processing circuitry, and/or processing unitmay be, or further include, memory and/or an integrated memory element,which may be a single memory device, a plurality of memory devices,and/or embedded circuitry of another processing module, module,processing circuit, processing circuitry, and/or processing unit. Such amemory device may be a read-only memory, random access memory, volatilememory, non-volatile memory, static memory, dynamic memory, flashmemory, cache memory, and/or any device that stores digital information.Note that if the processing module, module, processing circuit,processing circuitry, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,processing circuitry and/or processing unit implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, processing circuitry and/or processing unitexecutes, hard coded and/or operational instructions corresponding to atleast some of the steps and/or functions illustrated in one or more ofthe Figures. Such a memory device or memory element can be included inan article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with one or more other routines. In addition, a flow diagrammay include an “end” and/or “continue” indication. The “end” and/or“continue” indications reflect that the steps presented can end asdescribed and shown or optionally be incorporated in or otherwise usedin conjunction with one or more other routines. In this context, “start”indicates the beginning of the first step presented and may be precededby other activities not specifically shown. Further, the “continue”indication reflects that the steps presented may be performed multipletimes and/or may be succeeded by other activities not specificallyshown. Further, while a flow diagram indicates a particular ordering ofsteps, other orderings are likewise possible provided that theprinciples of causality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, a quantum register or otherquantum memory and/or any other device that stores data in anon-transitory manner. Furthermore, the memory device may be in a formof a solid-state memory, a hard drive memory or other disk storage,cloud memory, thumb drive, server memory, computing device memory,and/or other non-transitory medium for storing data. The storage of dataincludes temporary storage (i.e., data is lost when power is removedfrom the memory element) and/or persistent storage (i.e., data isretained when power is removed from the memory element). As used herein,a transitory medium shall mean one or more of: (a) a wired or wirelessmedium for the transportation of data as a signal from one computingdevice to another computing device for temporary storage or persistentstorage; (b) a wired or wireless medium for the transportation of dataas a signal within a computing device from one element of the computingdevice to another element of the computing device for temporary storageor persistent storage; (c) a wired or wireless medium for thetransportation of data as a signal from one computing device to anothercomputing device for processing the data by the other computing device;and (d) a wired or wireless medium for the transportation of data as asignal within a computing device from one element of the computingdevice to another element of the computing device for processing thedata by the other element of the computing device. As may be usedherein, a non-transitory computer readable memory is substantiallyequivalent to a computer readable memory. A non-transitory computerreadable memory can also be referred to as a non-transitory computerreadable storage medium.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device of astorage network, the method comprises: determining an encoded data slicereduction scheme for a set of encoded data slices stored in a set ofstorage units of the storage network, wherein a data segment of data isencoded into the set of encoded data slices in accordance with encodingparameters, and wherein the encoding parameters include a pillar widthnumber and a decode threshold number; and maintaining storage of the setof encoded data slices in accordance with the encoded data slicereduction scheme, wherein the maintaining storage includes keeping,until a deletion time for the set of encoded data slices, a number ofencoded data slices of the set of encoded data slices equal to orgreater than the decode threshold number and less than the pillar widthnumber.
 2. The method of claim 1, wherein the keeping the number ofencoded data slices includes deleting, in accordance with an explicitdeletion process, encoded data slices of the set of encoded data slicesover a period of time up until the deletion time.
 3. The method of claim1, wherein the maintaining storage further comprises implementing areduced rebuild operation of the encoded data slice reduction scheme by:determining a reduced rebuild number of encoded data slices such thatthe reduced rebuild number exceeds the decode threshold number and isless than pillar width number; and when less than the reduced rebuildnumber of encoded data slices is remaining: triggering a rebuild of oneor more encoded data slices of the set of encoded data slices.
 4. Themethod of claim 1 further comprises: setting the deletion time for theset of encoded data slices; and setting an encoded data slice reductiontime for the set of encoded data slices, wherein the encoded data slicereduction time is set at a time prior to the deletion time.
 5. Themethod of claim 4, wherein determining the encoded data slice reductionscheme is based on the encoded data slice reduction time being met. 6.The method of claim 4, wherein the maintaining storage of the set ofencoded data slices occurs during the encoded data slice reduction timeand ends at the deletion time.
 7. The method of claim 4, wherein thesetting the deletion time is based on a type of data of the set ofencoded data slices.
 8. The method of claim 4, wherein the setting thedeletion time is based on a user associated with the set of encoded dataslices.
 9. The method of claim 4, wherein the setting the deletion timeis based on storage capacity of the one or more sets of storage units.10. The method of claim 4, wherein the setting the encoded data slicereduction time is based on the deletion time.
 11. The method of claim 4,wherein the setting the encoded data slice reduction time is based on aminimum storage reliability level.
 12. The method of claim 4, whereinthe setting the encoded data slice reduction time is based on a userassociated with the set of encoded data slices.
 13. The method of claim4, wherein the setting the encoded data slice reduction time is based onstorage capacity of the one or more sets of storage units.
 14. Themethod of claim 1 further comprises: when the deletion time is reached:deleting, by the computing device, the set of encoded data slices. 15.The method of claim 1, wherein the encoding parameters include a reducedrebuild number that is greater than the decode threshold number.
 16. Themethod of claim 15, wherein the maintaining storage includes rebuildingan encoded data slice of the set of encoded data slices when anavailable number of encoded data slices is less than the reduced rebuildnumber.
 17. The method of claim 1 further comprises: a first time periodassociated with the maintaining storage, wherein during the first timeperiod a reduced rebuild operation of the encoded data slice reductionscheme is based on a first reduced rebuild number, wherein the firstreduced rebuild number is greater than the decode threshold number andless than the pillar width number; and during a second time periodassociated with the maintaining storage, the reduced rebuild operationis based on a second reduced rebuild number, wherein the second reducedrebuild number is less than first reduced rebuild number.
 18. The methodof claim 17, wherein the second time period is subsequent to the firsttime period.
 19. The method of claim 1, wherein the encoded data slicereduction scheme includes an explicit deletion.
 20. The method of claim1, wherein the encoded data slice reduction scheme includes a reducedrebuild operation.